Advanced full-waveform lidar data echo detection: Assessing quality of derived terrain and tree height models in an alpine coniferous forest

Abstract

Small footprint full-waveform airborne lidar systems hold large opportunities for improved forest characterisation. To take advantage of full-waveform information, this paper presents a new processing method based on the decomposition of waveforms into a sum of parametric functions. The method consists of an enhanced peak detection algorithm combined with an advanced echo modelling including Gaussian and generalized Gaussian models. The study focussed on the qualification of the extracted geometric information. Resulting 3D point clouds were compared to the point cloud provided by the operator. 40 to 60 % additional points were detected mainly in the lower part of the canopy and in the low vegetation. Their contribution to Digital Terrain Models (DTMs), Canopy Height Models (CHMs) was then analysed. The quality of DTMs and CHM-based heights was assessed using field measurements on black pine plots under various topographic and stand characteristics. Results showed only slight improvements, up to 5 cm bias and standard deviation reduction. However both tree crowns and undergrowth were more densely sampled thanks to the detection of weak and overlapping echoes, opening up opportunities to study the detailed structure of forest stands.

A Sparsity-Based Regularization Approach for Deconvolution of Full-Waveform Airborne Lidar Data

“…Generally, a set of Gaussian functions is considered to both fit to the received backscattered waveform and to characterize each pulse shape in this approach. Gaussian decomposition and other similar decomposition methods have been extensively used to interpret targets related to the backscattered waveform in urban and forested areas [1,2,17,25]. However, this method is considered challenging in the case of echoes with low signal strength (low SNR) and it is deficient in its calculation of the cross-section in complex waveforms.…”

“…Chauve et al [27] extracted target attributes from pulses by utilizing a mixture of the generalized Gaussian and Lognormal functions, with an improved global fitting for the former, and a better pulse fitting result locally in asymmetric cases for the latter. This work was extended by Chauve et al [17] to suppression of the ringing effect, and the results were utilized in DTM and Canopy Height Model (CHM) extraction, as well as for accurate determination of tree height. A low rate of height underestimation in the CHM generation was reported by the authors.…”

“…The method also requires initial determination of the number of targets [16,18,23,26], which is sometimes impractical or of high computational cost. In addition, a symmetric function might not always be sufficiently accurate to describe the target characteristics [17,27]. Furthermore, Gaussian decomposition has a vulnerability in that it can result in a null solution or even negative amplitudes in some cases [11,18].…”

LiDAR Utility for Natural Resource Managers

“…In practice, the high pulse density capability of current discrete-return systems should ensure adequate ground returns for most applications. Small footprint full-waveform LiDAR systems can provide more detailed information than discrete-return LiDAR systems in low vegetation and forest understories [122]. Data integration may provide another practical solution.…”

Full-Waveform LiDAR Point Cloud Land Cover Classification with Volumetric Texture Measures

“…The first is transferring all waveform samples into point cloud space to increase the number of 3D points. This results in denser point clouds which should be helpful in extracting more information for segmentation and classification tasks in both forest and urban areas (e.g., Chauve et al 2009;Lin et al 2010;Qin et al 2012). Echo detection to decompose and fit the waveform is the core of another development for deriving further valuable parameters and features.…”